Key Responsibilities:▪ Weekly Forecast of Warranty Provisions & Costs (Over/Under) – Efficiently performed ~80 Weekly Over/ Under forecast of warranty provisions to the tune of ~$ 27 Mn and costs to the tune of ~$20 Mn by considering the 3 Months/ 6 Months/ 1 Year actual wrench turning costs experienced by Warranty Material, Labor accounts in service channels in each region and calculating Over/ under run of costs as compared to the amortizations of the Provisions resulting from the Warranty Standards. Through use of Advanced Analytics and LEAN Problem-Solving Principles, I have been able to increase the accuracy of the estimate from ~80% to ~93%▪ Spotfire Warranty Estimate Dashboard Development & Warranty Datamart Enhancement – Leading Spotfire based Warranty Estimate Dashboard development in collaboration with GE Digital Spotfire team using GLPROD WARDEP & OBIEE Sales backlog report to Forecast amortization for Current & Future quarter based on sales orders and using actual ledger 3 Mos/ 6Mos/ 12 Mos costs averages to forecast warranty labor and material costs for current and future quarters. Providing functional leadership to Spotfire team by defining the requirements, validating the developments through user stories following Agile development methodology. Also, leading Warranty Datamart enhancement to include changes in operational P&L, SBU changes, EMEA region restructuring and Fx inclusion for warranty calculationAnnual Warranty Standard Setting Process for 2020, 2021 – Led Annual Warranty Standard setting process for ~4000 Planning names and ~ 100,000 PSI Codes across MICT, MR, Xray, Women’s Health, Surgery & IGS, Ultrasound SBUs across all regions by considering the most recent last 4 Qtrs of Warranty Material, Warranty Labor costs across service channels and using 4Qtr/ 8Qtr averages and pro rating it based on Install Base Months of equipment and the Warranty Terms

For US based one of world’s largest Chocolate manufacturer,Global Supply KPI DesignBusiness Context: As a part of Digital Transformation initiative project, worked on Global Supply KPI Design and reporting for one of the major business divisionsBusiness Challenge: To design Supply Control Tower for monitoring and decision making for close to 60 Key Performance metrics across 9 categories such as Case fill rate, Fill rate on time, Adherence to Schedule, OEE, Incident time loss, productivity, Mean time between stops, overhead costs, right first time, across different organizational levels such as Lines, factory, market, region levels and across various time periods such YTD, last 12 months moving average, last 3 periods etc.Solution Methodology- Understood the complex calculation methodology and provided functional expertise to the reporting and data engineering team to calculate each of 60 KPIs with around 90 input points at various organizational levels such as lines, factories, markets and regions and across various time periods such as 3 periods, YTD, Moving Annual Average etc. and designed solution to provide functionality of excluding certain lower organizational level data at line levels and how to dynamically handle the complexities arising out of sameResult and Benefits of pilot implementation- Resulted in single View of entire Supply organization Performance metrics across various organizational and time-based roll-ups covering ~ complex 60 KPIs and gave flexibility of addition of more KPIs in future

For India’s Largest Specialty Paper and Paper Boards Manufacturer,Manufacturing Process Analytics (Pulp & Paper)1. Business Challenge: To drive material cost reduction through reduction in GSM while meeting other Critical to Quality (CTQ) Parameters such as MD CD Stiffness, Moisture Content, Caliper etc.Solution Methodology- Developed a comprehensive process understanding of the entire Paper making process starting from Stock preparation at Wet End to Pressing, Drying, calendaring at Dry End. Understood the various process challenges and performed Gap analysis for Data Analytics based intervention.- Identified sources of data such as Paper reel production data, DCS process parameters online data, ShiftWise Offline process data, Offline and Online measured Quality data and stitched them on time basis to check for KPIV to KPOV relationship establishment and Baselining.Modeling and Simulation- Using classification, boundary conditions for Golden-batch was identified, and the disaggregated dataset after VIF based elimination method, thus, was used to build a model on R, using GAM (General Additive Model) technique- After model refining (improving R2 to above 0.7) using confusion matrix feedback loop, the model was used to predict the KPOVs for classified improvement batch. This was done basis simulation on R- Data enrichment, classification and aggregation, data stability check – distribution comparison check by statistical analysis, outlier detection & capping based on 1%ile and 99%ile , hypothesis building, Test-Training-Validation dataset preparation, data modelling – VIF, t-test for hypothesis testing, p-value cut off regression, R2-identificationBenefits of pilot implementation- During preliminary stochastic simulation ~60 % of Improvement Paper reels were upgraded to Golden batch reels resulting in reduction in GSM by 5% while maintaining all CTQ parameters as defined by customer resulting in material cost reduction of around ~ 2 Cr. INR (300k USD)

For India’s Largest Heavy Electrical Equipment Manufacturer,- As a Quality and Process Excellence Engineer, took up various process sigma improvement projects in the area of control equipment panel assembly, fabrication of heat sinks, copper bus bars, aluminum channels both in-house and subcontracted which involved data acquisition from various sources of data such as quality NCR records, Engineering Document Defects, SOPs and stitched them to form a comprehensive Datasets for analysis.- Applied DMAIC approach for baselining the existing sigma level of assembly process and fabrication processes which resulted in continuous improvement of sigma level from 5 to 5.8 in a span of 3 years- Performed process capability studies of fabrication machines CNC Punch machines, milling machines and baselined their CPk values for further improvement.